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import warnings
warnings.filterwarnings("ignore")
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import os.path as op
import sys
import contextlib
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from myst_nb import glue
sys.path.append("../../../../indicators_setup")
from ind_setup.plotting_int import plot_timeseries_interactive, fig_int_to_glue
from ind_setup.plotting import plot_bar_probs, fontsize
from ind_setup.tables import get_data_metrics, plot_df_table
sys.path.append("../../../functions")
from data_downloaders import GHCN
from data_downloaders import GHCN, download_oni_index
from ind_setup.plotting_int import plot_oni_index_th
from ind_setup.plotting import plot_bar_probs_ONI, add_oni_cat
import df2img
---------------------------------------------------------------------------
ModuleNotFoundError Traceback (most recent call last)
Cell In[2], line 12
9 from myst_nb import glue
11 sys.path.append("../../../../indicators_setup")
---> 12 from ind_setup.plotting_int import plot_timeseries_interactive, fig_int_to_glue
13 from ind_setup.plotting import plot_bar_probs, fontsize
14 from ind_setup.tables import get_data_metrics, plot_df_table
ModuleNotFoundError: No module named 'ind_setup'
country = 'Palau'
vars_interest = ['TMIN', 'TMAX']
update_data = False
path_data = "../../../data"
path_figs = "../../../matrix_cc/figures"
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if update_data:
df_country = GHCN.get_country_code(country)
print(f'The GHCN code for {country} is {df_country["Code"].values[0]}')
df_stations = GHCN.download_stations_info()
df_country_stations = df_stations[df_stations['ID'].str.startswith(df_country.Code.values[0])]
print(f'There are {df_country_stations.shape[0]} stations in {country}')
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if update_data:
GHCND_dir = 'https://www.ncei.noaa.gov/data/global-historical-climatology-network-daily/access/'
id = 'PSW00040309' # Koror Station
dict_min = GHCN.extract_dict_data_var(GHCND_dir, 'TMIN', df_country_stations.loc[df_country_stations['ID'] == id])[0][0]
dict_max = GHCN.extract_dict_data_var(GHCND_dir, 'TMAX', df_country_stations.loc[df_country_stations['ID'] == id])[0][0]
st_data = pd.concat([dict_min['data'], (dict_max['data'])], axis=1).dropna()
st_data['diff'] = st_data['TMAX'] - st_data['TMIN']
st_data['TMEAN'] = (st_data['TMAX'] + st_data['TMIN'])/2
st_data.to_pickle(op.join(path_data, 'GHCN_surface_temperature.pkl'))
else:
st_data = pd.read_pickle(op.join(path_data, 'GHCN_surface_temperature.pkl'))
st_data = st_data.resample('Y').mean()
glue("n_years", len(np.unique(st_data.index.year)), display=False)
glue("start_year", st_data.dropna().index[0].year, display=False)
glue("end_year", st_data.dropna().index[-1].year, display=False)
dict_plot = [{'data' : st_data, 'var' : 'TMEAN', 'ax' : 1, 'label' : 'TMEAN'},
]
dict_plot = [{'data' : st_data, 'var' : 'TMEAN', 'ax' : 1, 'label' : 'TMEAN'}]
fig = plot_timeseries_interactive(dict_plot, trendline=True, figsize = (25, 12))
glue("trend_fig_mean", fig_int_to_glue(fig), display=False)
st_data['TMEAN_ref'] = st_data['TMEAN'] - st_data.loc['1961':'1990'].TMEAN.mean()
# import matplotlib.pyplot as plt
# plot_bar_probs(x = st_data.index.year, y = st_data.TMEAN_ref, trendline = True, figsize = [15, 4])
# plt.title('Temperature anomalies (Over and above 1961 - 1990 reference period)', fontsize = 15);
nevents = 10
top_10 = st_data.sort_values(by='TMEAN_ref', ascending=False).head(nevents)
from ind_setup.tables import plot_df_table
var = 'TMEAN'
df = get_data_metrics(st_data, var, )
fig = plot_df_table(df.T, figsize = (300, 400))
fig, ax, trend = plot_bar_probs(x=st_data.index.year, y=st_data.TMEAN_ref, trendline=True,
y_label='Mean Temperature [°C]', figsize=[15, 4], return_trend=True)
glue("trend_mean", float(trend), display=False)
glue("change_mean", float(trend * len(np.unique(st_data.index.year))), display=False)
glue("top_10_year", float(top_10.sort_index().index.year[0]), display=False)
im = ax.scatter(top_10.index.year, top_10.TMEAN_ref,
c=top_10.TMEAN_ref.values, s=100, cmap='rainbow', label='Top 10 warmest years')
plt.title('Temperature anomalies (Over and above 1961 - 1990 reference period)', fontsize=15)
plt.colorbar(im).set_label('Mean Temperature [°C]', fontsize=fontsize)
glue("trend_fig", fig, display=False)
p_data = 'https://psl.noaa.gov/data/correlation/oni.data'
df1 = download_oni_index(p_data)
lims = [-.5, .5]
plot_oni_index_th(df1, lims = lims)
st_data_monthly = st_data.resample('M').mean()
st_data_monthly.index = pd.DatetimeIndex(st_data_monthly.index).to_period('M').to_timestamp() + pd.offsets.MonthBegin(1)
df1['tmin'] = st_data_monthly['TMIN']
df1['tmax'] = st_data_monthly['TMAX']
df1['tdiff'] = df1['tmax'] - df1['tmin']
df1['tmean'] = (df1['tmax'] + df1['tmin'])/2
df1['tmean_ref'] = df1['tmean'] - df1.loc['1961':'1990'].tmean.mean()
df1['tmean_ref_min'] = df1['tmean'] - df1.groupby(df1.index.year).max().tmean.min()
df1 = add_oni_cat(df1, lims = lims)
df2 = df1.resample('Y').mean()
fig = plot_bar_probs_ONI(df2, var='tmean_ref')
fig.suptitle('Temperature Anomaly over the 1961-1990 mean', fontsize = fontsize)
plt.savefig(op.join(path_figs, 'F2_ST_Mean.png'), dpi=300, bbox_inches='tight')
glue("fig_ninho", fig, display=False)
plt.show()
df_format = np.round(df1.describe(), 2)
fig = plot_df_table(df_format)
df2img.save_dataframe(fig=fig, filename="getting_started.png")
